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# GenAI Engineer

- **Company:** ApTask
- **Location:** New York, NY, United States
- **Salary:** $124,800.0 - $128,960.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Databases, Distributed Systems, Python (Programming Language), Data Streaming, Systems Integration, Data Logging, Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Build Management, Apache Kafka, Virtual Agents, Vulnerability Analysis
- **Published:** September 23, 2026
- **Apply:** https://www2.jobdiva.com/portal/?a=4ujdnwqsdebu7m13em5f0pt5dw80o500d7dv9cbq5ebzngb7yk0n43mjtefnbx0d&compid=0/jobs/29344037#/jobs/29344037

## About the Role

* Strong hands-on Python development
 * Proven experience building AI agents (GenAI, LLMs, tool usage, orchestration)
 * Experience with agent frameworks (e.g., LangChain, AutoGen, CrewAI) and MCP concepts
 * Solid architecture review and governance experience
 * Knowledge of distributed systems (Kafka, APIs, databases)
 * Understanding of security, access controls, and vulnerability assessment
 * Experience with observability (logging, monitoring) and performance engineering
 * Cloud experience (AWS/Azure/GCP)

Preferred:

 * Experience in architecture review boards / governance teams
 * Familiarity with enterprise architecture standards and compliance

## Description

We are looking for a hands-on Agentic AI Architect who can review and govern application architectures while also designing and building AI agents. This role requires strong architecture evaluation skills along with real-world experience in GenAI, agent frameworks, and Python development., * Review application architectures for alignment with enterprise standards and approved patterns

 * Identify deviations, design gaps, and recommend corrective actions
 * Assess security vulnerabilities, access patterns, and integrations (APIs, Kafka, databases, etc.)
 * Evaluate data flows, logging, monitoring, and performance engineering (PE) aspects
 * Design and build agentic AI solutions (single/multi-agent systems)
 * Develop and integrate AI agents using LLMs, tools, and enterprise systems
 * Provide guidance on scalable, secure, and reliable AI/GenAI architectures
 * Collaborate with engineering, security, and platform teams


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